The stability of imaging biomarkers in radiomics: a framework for evaluation

Wang, Helen Yu Chi; Donovan, Ellen M.; Nisbet, Andrew; South, C.; Alobaidli, Sheaka; Ezhil, Veni; Phillips, Iain; Prakash, Vineet; Ferreira, Mark; Webster, Philip; Evans, Philip

doi:10.1088/1361-6560/ab23a7

Cited by 12 publications

(21 citation statements)

References 29 publications

(45 reference statements)

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“…This is in line with recent publications that highlighted some vulnerabilities in the radiomic signature development, related to the risk of including features that are mainly correlated to the volume in prediction models. 35,38 Reproducibility of radiomic features was here assessed using the ICC metric, able to combine information about the degree of correlation and agreement between measurements. 34 This coefficient is one of the most adopted for the estimation of repeatability and reproducibility of radiomic indices, as reported in Traverso et al 8 In fact, it has been used also in the latest work considering T2w-MRI.…”

Section: Discussionmentioning

confidence: 99%

“…For this reason, it was not surprising that they are found as highly reliable, especially when volume is not modified in the considered conditions (as in the present study) or when the inter‐observer agreement is high (Dice > 0.9). This is in line with recent publications that highlighted some vulnerabilities in the radiomic signature development, related to the risk of including features that are mainly correlated to the volume in prediction models …”

Section: Discussionmentioning

confidence: 99%

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T2w‐MRI signal normalization affects radiomics features reproducibility

et al. 2020

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Purpose: Despite its increasing application, radiomics has not yet demonstrated a solid reliability, due to the difficulty in replicating analyses. The extraction of radiomic features from clinical MRI (T1w/T2w) presents even more challenges because of the absence of well-defined units (e.g. HU). Some preprocessing steps are required before the estimation of radiomic features and one of this is the intensity normalization, that can be performed using different methods. The aim of this work was to evaluate the effect of three different normalization techniques, applied on T2w-MRI images of the pelvic region, on radiomic features reproducibility. Methods: T2w-MRI acquired before (MRI1) and 12 months after radiotherapy (MRI2) from 14 patients treated for prostate cancer were considered. Four different conditions were analyzed: (a) the original MRI (No_Norm); (b) MRI normalized by the mean image value (Norm_Mean); (c) MRI normalized by the mean value of the urine in the bladder (Norm_ROI); (d) MRI normalized by the histogram-matching method (Norm_HM). Ninety-one radiomic features were extracted from three organs of interest (prostate, internal obturator muscles and bulb) at both time-points and on each image discretized using a fixed bin-width approach and the difference between the two time-points was calculated (Dfeature). To estimate the effect of normalization methods on the reproducibility of radiomic features, ICC was calculated in three analyses: (a) considering the features extracted on MRI2 in the four conditions together and considering the influence of each method separately, with respect to No_Norm; (b) considering the features extracted on MRI2 in the four conditions with respect to the inter-observer variability in region of interest (ROI) contouring, considering also the effect of the discretization approach; (c) considering Dfeature to evaluate if some indices can recover some consistency when differences are calculated. Results: Nearly 60% of the features have shown poor reproducibility (ICC < 0.5) on MRI2 and the method that most affected features reliability was Norm_ROI (average ICC of 0.45). The other two methods were similar, except for first-order features, where Norm_HM outperformed Norm_Mean (average ICC = 0.33 and 0.76 for Norm_Mean and Norm_HM, respectively). In the inter-observer setting, the number of reproducible features varied in the three structures, being higher in the prostate than in the penile bulb and in the obturators. The analysis on Dfeature highlighted that more than 60% of the features were not consistent with respect to the normalization method and confirmed the high reproducibility of the features between Norm_Mean and Norm_HM, whereas Norm_ROI was the less reproducible method. Conclusions:The normalization process impacts the reproducibility of radiomic features, both in terms of changes in the image information content and in the inter-observer setting. Among the considered methods, Norm_Mean and Norm_HM seem to provide the most reproducible features with respect to the ...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

T2w‐MRI signal normalization affects radiomics features reproducibility

et al. 2020

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“…Results from one disease site are not necessarily transferrable to another [ 108 ]. Expert ROI definition [ 103 ], multiple observers [ 103 , 104 , 108 ], identification of stable features with respect to delineation [ 90 , 104 , 105 ], automated segmentation [ 106 , 107 ], image filtering [ 108 ] Pre-processing F Number of grey levels used to discretize histogram and texture features affects feature values [ 96 , 98 , 109 ], as does bin width [ 94 ]. Texture features can be normalized to reduce dependency on the number of grey levels [ 98 ], number of grey levels used for discretization should be recorded with feature formula.…”

Section: Reported Methodological Limitations Of Ct Based Radiomics Stmentioning

confidence: 99%

“… Texture features can be normalized to reduce dependency on the number of grey levels [ 98 ], number of grey levels used for discretization should be recorded with feature formula. 128 grey levels may be optimal for texture features, along with thresholding [ 109 ] Feature extraction No studies found in the literature search. Feature correlation G Strong correlations between tumor volume and radiomic features exist [ 98 , [110] , [111] , [112] ] Normalization of features to volume [ 98 ], bit depth resampling [ 110 ], feature redesign [ 110 ], more robust statistics to check added value of radiomics signatures [ 111 ].…”

Section: Reported Methodological Limitations Of Ct Based Radiomics Stmentioning

confidence: 99%

Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

et al. 2020

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Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.

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“…This is particularly true for second and higher order texture features, transform based features, and features derived from unsupervised machine learning algorithms. How intensity values of CT and PET data is grouped together (aka binning) has been shown to have significant effects in the variability of texture features with optimal number of intensity values of 128 showing the least variability (38,39). Radiomic texture features derived from GLCM may be more robust compared to features derived from other matrices (40).…”

Section: Radiomic Feature Derivationmentioning

confidence: 99%

Narrative review of radiomics for classifying pulmonary nodules and potential impact on lung cancer screening

Stephens¹

2021

Curr Chall Thorac Surg

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Lung cancer screening has proven to be a useful tool for identifying early stage lung cancers, however, the overall accuracy can sometimes lead to false positive and negatives that have potential adverse effects on patient outcomes. Advancement in computational methods have allowed for quantification of pulmonary nodule imaging features, referred to as radiomics, which have the potential to increase lung cancer screening accuracy and improve patient management. The initial part of this review covers common radiomic features and the challenges in deriving them. The second part of this review systematically evaluates literature relating to radiomics and lung cancer finding articles in areas that might have the potential to change management in lung cancer screening. Pertinent literature included initial nodule classification as benign or malignant, classifying subsolid nodules as invasive or noninvasive, and prediction of tumor recurrence after surgical resection. The reviewed articles evaluating use of radiomics are mostly limited due to small sample sizes and lack of a validation cohort. These studies show potential for radiomic features to improve pulmonary nodule classification and change the way patients are managed, however, comparison between studies is limited due to variabilities in the way these features are derived. To make these features useful will require further research and standardization of the workflows that derive these features.

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The stability of imaging biomarkers in radiomics: a framework for evaluation

Cited by 12 publications

References 29 publications

T2w‐MRI signal normalization affects radiomics features reproducibility

T2w‐MRI signal normalization affects radiomics features reproducibility

Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

Narrative review of radiomics for classifying pulmonary nodules and potential impact on lung cancer screening

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